
The scale parameter \(s\)  and the shift parameter \(b\)  are predicted by a neural network using \(h_t\) . We adapt a data-dependent initialization scheme for this network [1]}. Specifically, we initialize the above neural network such that the predicted \(s\)  and \(b\)  yield \(f(z)\)  with unit variance and zero mean for the first batch. We can compute \(|det(J_f)| = \prod _i |s(i)|\) .
